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COMPUTER SCIENCE APPLICATIONS BY PHILIP KLEIN PDF the very best book Coding The Matrix: Linear Algebra Through Computer. Editorial Reviews. About the Author. Philip Klein is Professor of Computer Science at Brown University. He was a recipient of the National Science Foundation's. Matrix-vector multiplication in terms of linear combinations. Linear-Combinations linear because it is based on linear algebra,. ▷ binary because the input.

If you want to cover the basics of LA vector and matrix manipulation, mainly , and want to have some practical application of that knowledge - there are two main areas which can be easily explored at home: Machine learning particularly neural networks For the first, don't just start playing with OpenGL or Direct3D - while you need to know the math on those, you won't get your feet as wet. What you want to do is start from the bottom and build up essentially building a software 3D engine. While you won't be generally dealing with large matrices or vectors 4x4 mainly , it will be more than plenty to teach the bare ropes. Machine learning - and neural networks - are where you start to deal with much larger matrices, as they hold the mathematical representation of the nodes which make up the graph that is the network. Now you have shift gears and think about how to parallelize things, on a much potentially larger scale even here, though, you can start out small - a simple NN to learn the XOR function is very small, but contains everything needed to move on to larger networks once you understand the basics. Again - these two practical applications one touch the surface of LA, but are both fun applications of these basics to perhaps motivate you to learn more. Even if you don't take it to the next level though, what you gain from these experiments might prove invaluable in the future. Personally, I think they should emphasize these two applications in lower grades when they start to teach this stuff; I know when I was in high school too many years ago to contemplate , the only thing that kept me interested in both my geometry and linear algebra sections was the fact that I was playing around with 3D wireframe graphics on my 8-bit microcomputer at home, and needed to understand the stuff!

After watching those, I started this course: In my opinion, the latter is one of the best math courses available on YouTube, and definitely deserves more views.

I'm just finished watching the 3blue 1brown series and am working through the Klein book now. Klein's books is very heavy on symbolic manipulation and not much geometric intuition. I still think it's worthwhile because of its CS applications.

But someone wanting to learn LA can easily get discouraged. I would suggest one do both the book and the 3blue 1brown series. The former is the bread, the latter is the jam. Klein emphasizes practical computer science applications of LA like principal components and hands-on coding tasks , whereas Strang emphasizes LA in terms of calculus and vector calculus. I think both courses are outstanding. I suspect CS students will appreciate Klein's content and examples more, though Strang lectures are so good you won't find much to complain about.

I have heard that some math purists object to Strang's emphasis as being as lacking fundamental rigor and overemphasizing intuition. But this criticism probably applies to both courses.

I have an engineering background. I think this is a fair characterization of the two approaches. Has anyone here already taken a similar path and what did you think?

My main interests are in graphics programming, so I'm hoping to apply what I learn from the course to that.

If anyone else has any recommendations on other areas of math, courses, or books in general for learning CG, that would be much appreciated! Suggestions 1.

When Life is Linear: I'll check them out, thanks! The approaches of Strang and Klein are complementary.

The exercises in that chapter of the Klein are brilliant. Strang sticks with the reals. For this topic, the Klein suffers for lack of straight-up problem drills. Thanks for the comparison. Send mail if you have questions about using the book for a university course.

Here are examples of applications addressed in Coding the Matrix. Error-correcting codes Error-correcting codes are used, e. At the core of the most sophisticated integer-factoring algorithms is a simple problem in linear algebra.

Audio and image compression Compression of audio and images aids efficient storage and transmission.

Coding The Matrix: Course Resources Data and support code required for carrying out the assignments are provided here. Join the mailing list for updates about addition of resources.

Slides Slides from the course taught at Brown University in Fall Edition 1 of the textbook is available for download. It incorporates corrections and a revised and expanded index.

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